Model-Based Control Framework For Energy Storage Integration In Frequency-Regulated Smart Grids
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Abstract
The increasing penetration of renewable energy sources (RES) in modern power networks has introduced significant challenges in maintaining frequency stability due to the intermittent and uncertain nature of renewable generation. To address this issue, battery energy storage systems (BESS) have emerged as a key technology for rapid frequency support and grid balancing. This paper presents a comprehensive model-based control framework for integrating BESS into frequency-regulated smart grids. The proposed approach develops a detailed dynamic model of the grid-connected storage system, including converter dynamics, battery state-of-charge behavior, and power exchange mechanisms. Three control strategies—conventional proportional–integral (PI) control, proportional power control (PPC), and model predictive control (MPC)—are benchmarked to evaluate performance under load disturbances and renewable fluctuations. Simulation-based analysis demonstrates that the model predictive control approach exhibits faster frequency restoration, reduced overshoot, and better energy utilization compared to conventional methods. The framework also investigates the impact of BESS droop coefficients and storage capacity on system stability, highlighting the trade-off between control aggressiveness and energy efficiency. Results further indicate that the integration of BESS improves frequency nadir, reduces rate-of-change-of-frequency (ROCOF), and enhances grid resilience under dynamic operating conditions. The proposed model-based framework provides a reliable foundation for designing and optimizing intelligent frequency regulation strategies in smart grids with high renewable energy penetration.
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